: Hao Zhou, Kefa Cen
: Combustion Optimization Based on Computational Intelligence
: Springer-Verlag
: 9789811078750
: Advanced Topics in Science and Technology in China
: 1
: CHF 87.50
:
: Wärme-, Energie- und Kraftwerktechnik
: English
: 291
: Wasserzeichen/DRM
: PC/MAC/eReader/Tablet
: PDF

This book presents the latest findings on the subject of combustion optimization based on computational intelligence. It covers a broad range of topics, including the modeling of coal combustion characteristics based on artificial neural networks and support vector machines. It also describes the optimization of combustion parameters using genetic algorithms or ant colony algorithms, an online coal optimization system, etc. Accordingly, the book offers a unique guide for researchers in the areas of combustion optimization, NOx emission control, energy and power engineering, and chemical engineering.



Professor Hao Zhou received his Ph.D. degree from Zhejiang University in 2004. He is currently Deputy Director of  State Key Laboratory of Clean Energy Utilization at Zhejiang University and Director of the Zhejiang University - University of Leeds joint research center for sustainable energy. His research interests include combustion optimization, low pollutant combustion technology for utility boilers, and neural network and support vector machine modeling methods. He has published over 20 academic papers and filed 7 patents in the areas of combustion pollutants control and combustion optimization since 2000.

Professor Kefa Cen is a member of the Chinese Academy of Engineering. He received his Ph.D. degree from Moscow Industrial Technology University and has expertise in clean coal combustion and gasification, poly-generation and comprehensive utilization of energy resources, as well as biomass gasification and bio-oil. He is currently Director of the Institute for Thermal Power Engineering at Zhejiang University and Chairman of the Chinese Society of Power Engineering's International Cooperation& Exchange Committee. He is also Editor-in-Chief of the Journal of Zhejiang University (Engineering Science) and the Journal of Renewable Energy. He has published over 800 academic papers and 15 books.
Preface6
Contents7
About the Authors11
List of Figures12
List of Tables24
1 Introduction26
Abstract26
1.1 Background26
1.2 Coal Combustion27
1.2.1 General Process of Coal Combustion27
1.2.2 The Duration of Coal Combustion27
1.2.3 The Characteristic of Coal Combustion28
1.3 Carbon Burnout29
1.4 Coal Combustion Optimization30
1.5 Outline of the Book30
References31
2 The Influence of Combustion Parameters on NOx Emissions and Carbon Burnout32
Abstract32
2.1 Introduction32
2.2 Influence of Combustion Parameters on NOx Emissions33
2.3 Influence of Combustion Parameters on Carbon Burnout38
References44
3 Modeling Methods for Combustion Characteristics45
Abstract45
3.1 Introduction45
3.2 Experimental Method46
3.2.1 Experimental Methods of Coal Combustion Characteristics Study46
3.2.1.1 Coal Combustion Characteristics46
3.2.1.2 Experimental Methods46
3.2.1.3 Test System of Coal Combustion53
3.2.2 Flame Temperature Measurement57
3.2.3 Flue Gas Analysis58
3.2.4 Application Examples62
3.3 CFD Method89
3.3.1 Turbulence Model90
3.3.2 Combustion Model93
3.3.3 Radiative Heat Transfer Model94
3.3.4 Discrete Phase Model94
3.3.5 Reaction Models of Particles95
3.3.6 Pollutant Formation Model96
3.3.7 Application Examples96
3.4 Computational Intelligence Method156
3.5 Summary166
References166
4 Neural Network Modeling of Combustion Characteristics170
Abstract170
4.1 Introduction170
4.1.1 Structural Model of Neuron170
4.1.2 MP Model171
4.2 Back Propagation Neural Network Method172
4.2.1 BPNN Algorithm172
4.2.2 Learning Methods173
4.3 General Regression Neural Network Method174
4.3.1 GRNN Algorithm175
4.3.2 GRNN Structure175
4.4 Comparison of BPNN Method and GRNN Method176
4.4.1 GRNN Advantages176
4.4.2 Comparison on Example176
4.5 Summary177
References177
5 Classification of the Combustion Characteristics based on Support Vector Machine Modeling178
Abstract178
5.1 The Introduction of Support Vector Machine178
5.2 The Principle of Support Vector Machine180
5.2.1 Support Vector Classification180
5.2.2 Support Vector Regression181
5.2.3 Kernel Function181
5.3 The Application of Support Vector Machine182
5.3.1 Coal Identification182
5.3.2 The Prediction of Ash Fusion Temperature184
5.3.3 The Prediction of Unburned Carbon in Fly Ash186
5.3.4 The Prediction of NOx Emission188
5.4 Summary192
References192
6 Combining Neural Network or Support Vector Machine with Optimization Algorithms to Optimize the Combustion194
Abstract194
6.1 Introduction of Optimization Algorithms194
6.1.1 Genetic Algorithms194
6.1.1.1 Introduction to GA194
6.1.1.2 The Description of GA195
6.1.1.3 The Process of GA Approach195
6.1.2 Ant Colony Algorithms196
6.1.2.1 Introduction to ACO196
6.1.2.2 The Description of ACO196
6.1.2.3 Another Algorithm of ACO199
6.1.3 Particle Swarm Algorithms201
6.2 Combining Neural Network and GA to Optimize the Combustion203
6.2.1 Experiments203
6.2.2 Result and Discussions205
6.2.3 Conclusions210
6.3 Combining SVM and Optimization Algorithms to Optimize the Combustion210
6.3.1 Modeling NOx Emissions by SVM and ACO with Operating Parameters Optimizing211
6.3.1.1 Experimental Setup and Data Analysis211
6.3.1.2 Results214
6.3.1.3 Prediction Results of ACO–SVR214
6.3.1.4 Prediction Results of Grid SVR218
6.3.1.5 Comparison and Discussion220
6.3.1.6 Conclusions222
6.3.2 Modeling NOx Emissions by SVM and PSO with Model and Operating Parameters Optimizing223
6.3.2.1 Experimental Setup223
6.3.2.2 Optimization Results for the Boiler Load of 288.45 MW227
6.3.2.3 Comparison with Other Methods228
6.3.2.4 Conclusions231
6.3.3 Comparison of Optimization Algorithms for Low NOx Combustion232
6.3.3.1 Experimental Setup and NOx Emission Data232
6.3.3.2 Estimation of NOx Emissions by SVR234
6.3.3.3 Selection of Model Parameters235
6.3.3.4 NOx E